scholarly journals Mid-latitude ozone monitoring with the GOMOS-ENVISAT experiment version 5: the noise issue

2010 ◽  
Vol 10 (23) ◽  
pp. 11839-11849 ◽  
Author(s):  
P. Keckhut ◽  
A. Hauchecorne ◽  
L. Blanot ◽  
K. Hocke ◽  
S. Godin-Beekmann ◽  
...  
Keyword(s):  
Stratospheric Ozone ◽  
Bimodal Distribution ◽  
Recovery Phase ◽  
New Method ◽  
Light Conditions ◽  
Temperature Spectrum ◽  
Background Light ◽  
Spectrum Shape ◽  
Ozone Monitoring

Abstract. The GOMOS ozone profiles have been analysed to evaluate the GOMOS ability to capture the long-term ozone evolution at mid-latitudes during the expected recovery phase of the ozone layer. Version 5 of the operational GOMOS ozone data has been compared with data from two of the longest ground-based instruments based on different techniques and already involved with many other previous space instrument validations. Comparisons between ground-based and GOMOS data confirm the occurrence of spurious retrievals mainly occurring since 2006. Using a selected set of data it is shown that some bad retrievals are induced by the increasing dark charge of the detectors combined with an inadequate method for its correction. This effect does not only induce a continuous bias, but is rather exhibiting a bimodal distribution including the correct profiles and the bad retrievals. For long-term analyses it is recommended filtering the data according to background light conditions and star temperature (spectrum shape). The new method of the dark charge estimate proposed to be implemented in the version 6 of the ESA algorithm seems to significantly reduce the occurrence of such effects and should allow to monitor stratospheric ozone using GOMOS data with greater confidence.

scholarly journals Ozone monitoring with the GOMOS-ENVISAT experiment version 5

2010 ◽  
Vol 10 (6) ◽  
pp. 14713-14735 ◽  
Author(s):  
P. Keckhut ◽  
A. Hauchecorne ◽  
L. Blanot ◽  
K. Hocke ◽  
S. Godin-Beekmann ◽  
...  
Keyword(s):  
Stratospheric Ozone ◽  
Bimodal Distribution ◽  
Recovery Phase ◽  
New Method ◽  
Ozone Monitoring ◽  
Term Analysis

Abstract. The GOMOS ozone profiles derived have been analyzed to evaluate the GOMOS ability to capture the long-term ozone evolution during its expected recovery phase. Version 5 of the GOMOS data has been compared with two of the longest ground-based instruments based on different techniques and already involved with many other previous space instrument validations. Increasing differences reported in 2006 indicate that some of the retrieved profiles are strongly biased. This bad retrieval is probably due to the increasing dark charge of the detectors combined with an inadequate method for its correction. This effect does not induce a continuous bias but is rather exhibiting a bimodal distribution including the correct profiles and the bad retrievals. For long-term analysis it is recommended to filter the data accordingly. The new method of dark charge estimate that is proposed to be implemented in the version 6 of the ESA algorithm, seems to reduce significantly the occurrence of such effects and will allow to monitor stratospheric ozone using GOMOS data with better confidence.

Machine-Learning-Based Satellite-Corrected Global Stratospheric Ozone Profile Dataset from a Chemical Transport Model

2021 ◽  
Author(s):  
Sandip Dhomse ◽  
Martyn Chipperfield
Keyword(s):  
Machine Learning ◽  
Transport Model ◽  
Stratospheric Ozone ◽  
Chemical Transport ◽  
Recovery Phase ◽  
Horizontal Resolution ◽  
Chemical Transport Model ◽  
High Quality ◽  
Ozone Profile

<p>High quality global ozone profile datasets are necessary to monitor changes in stratospheric ozone. Hence, various methodologies have been used to merge and homogenise different satellite datasets in order to create long-term observation-based ozone profile datasets with minimal data gaps. However, individual satellite instruments use different measurement methods and retrieval algorithms that complicate the merging of these different datasets. Furthermore, although atmospheric chemical models are able to simulate chemically consistent long-term datasets, they are prone to the deficiencies associated with the computationally expensive processes that are generally represented by simplified parameterisations or uncertain parameters.</p><p>Here, we use chemically consistent output from a 3-D Chemical Transport Model (CTM, TOMCAT) and an ensemble of three machine learning (ML) algorithms (Adaboost, GradBoost, Random Forest), to create a 42-year (1979-2020) stratospheric ozone profile dataset. The ML algorithms are primarily trained using the Stratospheric Water and OzOne Satellite Homogenized (SWOOSH) dataset by selecting the UARS-MLS (1992-1998) and AURA-MLS (2005-2019) time periods. This ML-corrected version of monthly mean zonal mean TOMCAT (hereafter ML-TOMCAT) ozone profile data is available at both pressure (1000 hPa - 1 hPa) and geometric height (surface to 50 km) levels at about 2.5 degree horizontal resolution.</p><p>We will present a detailed evaluation of ML-TOMCAT profiles against range of merged satellite datasets (e.g. GOZCARDS, SAGE-CCI-OMPS, and BVertOzone) as well high quality solar occultation observations (e.g. SAGE-II v7.0 (1984-2005), HALOE v19 (1991-2005) and ACE v4.1 (2004-2020). Overall, ML-TOMCAT shows good agreement with the evaluation datasets but with poorer agreement at low latitudes. We also show that, as in different merged satellite data sets, ML-algorithms show larger spread in the tropical middle stratosphere. Finally, we will present a trend analysis from TOMCAT and ML-TOMCAT profiles for the post-1998 ozone recovery phase.</p>

scholarly journals Reevaluation of stratospheric ozone trends from SAGE II data using a simultaneous temporal and spatial analysis

2014 ◽  
Vol 14 (24) ◽  
pp. 13455-13470 ◽  
Author(s):  
R. P. Damadeo ◽  
J. M. Zawodny ◽  
L. W. Thomason
Keyword(s):  
Spatial Analysis ◽  
Stratospheric Ozone ◽  
Recovery Period ◽  
New Method ◽  
Data Sets ◽  
Sampled Data ◽  
Data Set ◽  
Multiple Data Sets ◽  
Temporal And Spatial

Abstract. This paper details a new method of regression for sparsely sampled data sets for use with time-series analysis, in particular the Stratospheric Aerosol and Gas Experiment (SAGE) II ozone data set. Non-uniform spatial, temporal, and diurnal sampling present in the data set result in biased values for the long-term trend if not accounted for. This new method is performed close to the native resolution of measurements and is a simultaneous temporal and spatial analysis that accounts for potential diurnal ozone variation. Results show biases, introduced by the way data are prepared for use with traditional methods, can be as high as 10%. Derived long-term changes show declines in ozone similar to other studies but very different trends in the presumed recovery period, with differences up to 2% per decade. The regression model allows for a variable turnaround time and reveals a hemispheric asymmetry in derived trends in the middle to upper stratosphere. Similar methodology is also applied to SAGE II aerosol optical depth data to create a new volcanic proxy that covers the SAGE II mission period. Ultimately this technique may be extensible towards the inclusion of multiple data sets without the need for homogenization.

The problem of ballistic design of the space system Earth remote sensing in stable near-circular sun-synchronous orbits with long-term spacecraft lifetime

2018 ◽  
pp. 3-9
Author(s):  
V. M. Artyushenko ◽  
D. Y. Vinogradov
Keyword(s):  
Remote Sensing ◽  
Target Function ◽  
Normal Operation ◽  
Space Systems ◽  
Light Conditions ◽  
Space System ◽  
Earth Remote Sensing ◽  
Synchronous Orbit ◽  
Favorable Conditions

The article deals with the issues related to the problem of ballistic design of the space system of remote sensing of the Earth on stable near-circular solar-synchronous orbits with long-term existence of spacecraft. We propose a rational method of maintaining a solar-synchronous orbit in given light conditions with prolonged active lifetime of space systems. In solving this problem, the total time of normal operation of the system for a given period of operation, during which the most favorable conditions for the use of spacecraft are provided on the main parts of orbits, is taken as a target function.

scholarly journals Novel robust time series analysis for long-term and short-term prediction

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hiroshi Okamura ◽  
Yutaka Osada ◽  
Shota Nishijima ◽  
Shinto Eguchi
Keyword(s):  
Robust Regression ◽  
Simulated Data ◽  
New Method ◽  
Short Term ◽  
Least Absolute Deviations ◽  
Term Prediction ◽  
Regression Approach ◽  
Short Term Prediction ◽  
Long Term Prediction

AbstractNonlinear phenomena are universal in ecology. However, their inference and prediction are generally difficult because of autocorrelation and outliers. A traditional least squares method for parameter estimation is capable of improving short-term prediction by estimating autocorrelation, whereas it has weakness to outliers and consequently worse long-term prediction. In contrast, a traditional robust regression approach, such as the least absolute deviations method, alleviates the influence of outliers and has potentially better long-term prediction, whereas it makes accurately estimating autocorrelation difficult and possibly leads to worse short-term prediction. We propose a new robust regression approach that estimates autocorrelation accurately and reduces the influence of outliers. We then compare the new method with the conventional least squares and least absolute deviations methods by using simulated data and real ecological data. Simulations and analysis of real data demonstrate that the new method generally has better long-term and short-term prediction ability for nonlinear estimation problems using spawner–recruitment data. The new method provides nearly unbiased autocorrelation even for highly contaminated simulated data with extreme outliers, whereas other methods fail to estimate autocorrelation accurately.

scholarly journals Automated Microscopy: Macro Language Controlling a Confocal Microscope and its External Illumination: Adaptation for Photosynthetic Organisms

2016 ◽  
Vol 22 (2) ◽  
pp. 258-263 ◽  
Author(s):  
Gábor Steinbach ◽  
Radek Kaňa
Keyword(s):  
Light Sources ◽  
Light Conditions ◽  
Photosynthetic Organisms ◽  
Automated Microscopy ◽  
Photosynthetic Microorganisms ◽  
Arduino Microcontroller ◽  
Microscopy Data ◽  
Kinetics Of ◽  
Photosynthetic Cells

AbstractPhotosynthesis research employs several biophysical methods, including the detection of fluorescence. Even though fluorescence is a key method to detect photosynthetic efficiency, it has not been applied/adapted to single-cell confocal microscopy measurements to examine photosynthetic microorganisms. Experiments with photosynthetic cells may require automation to perform a large number of measurements with different parameters, especially concerning light conditions. However, commercial microscopes support custom protocols (throughTime Controlleroffered by Olympus orExperiment Designeroffered by Zeiss) that are often unable to provide special set-ups and connection to external devices (e.g., for irradiation). Our new system combining an Arduino microcontroller with theCell⊕Findersoftware was developed for controlling Olympus FV1000 and FV1200 confocal microscopes and the attached hardware modules. Our software/hardware solution offers (1) a text file-based macro language to control the imaging functions of the microscope; (2) programmable control of several external hardware devices (light sources, thermal controllers, actuators) during imaging via the Arduino microcontroller; (3) theCell⊕Findersoftware with ergonomic user environment, a fast selection method for the biologically important cells and precise positioning feature that reduces unwanted bleaching of the cells by the scanning laser.Cell⊕Findercan be downloaded fromhttp://www.alga.cz/cellfinder. The system was applied to study changes in fluorescence intensity inSynechocystissp. PCC6803 cells under long-term illumination. Thus, we were able to describe the kinetics of phycobilisome decoupling. Microscopy data showed that phycobilisome decoupling appears slowly after long-term (>1 h) exposure to high light.

scholarly journals Reconciling differences in stratospheric ozone composites

2017 ◽  
Vol 17 (20) ◽  
pp. 12269-12302 ◽  
Author(s):  
William T. Ball ◽  
Justin Alsing ◽  
Daniel J. Mortlock ◽  
Eugene V. Rozanov ◽  
Fiona Tummon ◽  
...  
Keyword(s):  
Time Series ◽  
Likelihood Function ◽  
Stratospheric Ozone ◽  
Gaussian Mixture ◽  
Robust Estimate ◽  
Mixture Density ◽  
Source Data ◽  
Ozone Trends ◽  
Decadal Trend

Abstract. Observations of stratospheric ozone from multiple instruments now span three decades; combining these into composite datasets allows long-term ozone trends to be estimated. Recently, several ozone composites have been published, but trends disagree by latitude and altitude, even between composites built upon the same instrument data. We confirm that the main causes of differences in decadal trend estimates lie in (i) steps in the composite time series when the instrument source data changes and (ii) artificial sub-decadal trends in the underlying instrument data. These artefacts introduce features that can alias with regressors in multiple linear regression (MLR) analysis; both can lead to inaccurate trend estimates. Here, we aim to remove these artefacts using Bayesian methods to infer the underlying ozone time series from a set of composites by building a joint-likelihood function using a Gaussian-mixture density to model outliers introduced by data artefacts, together with a data-driven prior on ozone variability that incorporates knowledge of problems during instrument operation. We apply this Bayesian self-calibration approach to stratospheric ozone in 10° bands from 60° S to 60° N and from 46 to 1 hPa (∼ 21–48 km) for 1985–2012. There are two main outcomes: (i) we independently identify and confirm many of the data problems previously identified, but which remain unaccounted for in existing composites; (ii) we construct an ozone composite, with uncertainties, that is free from most of these problems – we call this the BAyeSian Integrated and Consolidated (BASIC) composite. To analyse the new BASIC composite, we use dynamical linear modelling (DLM), which provides a more robust estimate of long-term changes through Bayesian inference than MLR. BASIC and DLM, together, provide a step forward in improving estimates of decadal trends. Our results indicate a significant recovery of ozone since 1998 in the upper stratosphere, of both northern and southern midlatitudes, in all four composites analysed, and particularly in the BASIC composite. The BASIC results also show no hemispheric difference in the recovery at midlatitudes, in contrast to an apparent feature that is present, but not consistent, in the four composites. Our overall conclusion is that it is possible to effectively combine different ozone composites and account for artefacts and drifts, and that this leads to a clear and significant result that upper stratospheric ozone levels have increased since 1998, following an earlier decline.

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